基于MIP图像拼接系统的大型古生物化石数字化应用研究北大核心CSCD

在大型古生物化石数字化过程中,为了充分展示化石的细节信息,往往需要拍摄大量的图像。为了实现大型古生物化石数字化数据的完整性,需要对这些大量的图像进行精密的图像拼接处理。基于这种应用需求的前提下,本文在自主研发的Mosaic of Image Program(MIP)图像拼接系统的基础上,对高精度的相机检校、畸变检校及改正和拼接缝的保真处理等方面进行研究,形成系统的古生物化石彩色合成影像数字化流程。在宜州化石馆的实际处理中,完成了杨氏锦州龙、蜥脚类恐龙、孔子鸟等大型古生物化石的数字化,几何失真小于0.36mm(畸变矫正精度优于1像元,拼接精度优于2像元,像片分辨率0.12mm)。同时采用基于SIFT的自动辐射归一化处理算法对拼接影像进行辐射均衡处理,矫正拼接影像辐射亮度的不均衡。     

鼻咽癌细胞双光子显微图像处理

鼻咽细胞的双光子显微图像中含有着丰富信息,借助计算机和图像处理算法可进行分析处理。图象分割是双光子显微图象处理中的一项重要技术,至今为止尚未形成一个最佳通用方法,也没有定义出双光子显微图象分割的统一标准。本文首先采用噪声干扰法进行去噪,采用低帽的变换等的数学形态学来增强鼻咽癌细胞图像,使细胞更加容易分辨,接着对几种经典边缘检测算法进行讨论比较,紧接着根据鼻咽双光子显微图像的实际特征,采取腐蚀算法求出鼻咽癌细胞边缘。然后进行区域生长定位细胞,并采用一些改进的判别分析算法和区域面积算法对鼻咽癌细胞进行阈值分割,获得较好结果。     

基于恒定空间分辨率离散序列小波变换的眼底血管造影图像拼接方法

将恒定空间分辨率离散序列小波变换（discrete sequence wavelet transform,DSWT)应用于眼底吲哚青绿血管造影（indocyanine green angiography,ICGA)图像的拼接,解决了传统基为2的DSWT会导致分解结果的空间分辨率下降的问题。提出对图像小波分解细节逼近和平滑逼近分别使用加权平均拼接和直接平均拼接进行处理的策略,以得到兼顾视觉效果和保真性的拼接结果。并且针对眼底图像背景光照不一致,提出在小波域进行处理的策略。实验结果表明拼接算法效果良好。     

基于SIFT特征和近似最近邻算法的医学CT图像检索

针对医学X线计算机断层（Computed Tomography,CT）图像,提出了一种基于尺度不变特征变换（Scale InvariantFeature Transform,SIFT）特征和近似最近邻算法的检索方法。首先通过SIFT算法得到图像的特征点和相应的特征向量,再采用近似最近邻算法进行SIFT特征向量的匹配搜索,得到数据库中与参考图像最相似的图像序列。实验结果表明,该法能检索到与目标图像细节相符的结果,大大提高了检索速度。与传统的基于纹理的检索方法相比,查准率和检索结果与目标图像的相似程度方面更佳,符合医学CT图像检索的要求。     

基于图像处理的血液细胞特征提取

利用数学形态学知识和图像处理方法,对缺铁性贫血的血液显微图像进行了分析,编制了相应的计算程序,对选取的区域内细胞的个数、半径和面积等重要参数进行了统计和处理,这对进一步研究细胞及其组织变化、医学临床诊断等问题,具有一定的指导意义。     

牙形石数字图像采集与图像增强方法研究

化石自动鉴定研究是古生物学研究的一个前沿方向。数字图像的采集与处理是化石自动鉴定的基础,处理后的图像质量直接影响了计算机识别的效果。文中以牙形石为例,通过对化石数字图像采集方法的研究,获得了光学显微图像和扫描图像两类牙形石数字图像。详细介绍了灰度变换、均值滤波、中值滤波和微分算子等图像增强方法,通过实验比较了各自的优缺点。实验结果表明,这些方法可以有效改善牙形石数字图像质量。选择合适的图像增强方法能够突出牙形石的细节,有利于牙形石的特征观察和进一步的分析,为后续的牙形石自动鉴定研究奠定了基础。     

数码摄像机在显微镜教学中的应用

利用数码摄像机将显微镜内的显微图像转变成视频信号,再传输到电视机或电脑中,通过电视机或投影机银幕展示给学生观察,实现非数码显微镜关于显微图像的演示和贮存功能。     

用家用数码照相机直接经显微镜目镜摄取高质量的数码图像

在生物学和医学的科研、教学中，往往需要使用生物显微镜和解剖镜。而与之相配套的照相装置，是保存、发表和教学展示光学显微图像的必备设备。一般的显微照相装置需要配有专用的光学适配装置，用于特定型号的显微镜，拥有成套设备的成本较高。本文探讨了使用普通家用数码照相机直接经生物显微镜目镜，摄取高质量、高分辨率数码光学显微图像的可行性和影响因素。     

跨膜运输静、动态图像特征识别的比较

图像在生物学教学中起着越来越重要的作用,然而对于学生如何识别图像却不甚明了。为了解学生对图像特征识别的情况,本研究邀请不同年级学生分组分别观看不同形式的跨膜运输图像并让学生描述图像展现的内容。基于“变异理论”形成的跨膜运输图像关键特征的判别标准,对学生的描述进行分析比较发现教学中运用多种视觉表征形式更利于达到教学目标。     

基于微血栓运动分析的微血管特征结构自动提取

提出了一种基于微血栓运动分析的微血管特征结构自动提取策略。提出用灰度梯度直方图统计来自动选阈的快速阈值值化算法,检测形态复杂的微血管图像边缘,抑制次要的微血管,采用低阈值双窗二次角点选择策略选取边缘曲线角点。通过微血管显微图像及其二值化图像分析,建立反映含微血栓的微血管特征结构模型,利用微血管的先验知识,给出提取微血管特征结构的算法,最后给出微血管显微图像结构的提取结果,实验证明该算法是十分有效的。含微血栓的微血管的特征结构建立,复杂的微血栓的匹配和识别问题将得到简化,微血管及微血栓的形态变化及运动估算任务得以减轻。该研究对于脑微循环障碍和老年病的基础医学研究和临床实践具有十分重要的意义。     

Comparison of three hexagonal tessellations through extraction of blood cell geometric features

Although image data are almost universally acquired on rectangular sampling lattices, the regular hexagonal lattice offers important theoretical advantages for tessellation of images, particularly when subsequent processing involves operations on local image neighborhoods. The few systems capable of processing hexagonally tessellated images have approximated this tessellation by using image data acquired on a rectangular sampling lattice, from which six of the eight image samples were selected from each local neighborhood. This paper describes a simple method of directly acquiring image data in hexagonal image tessellations; the method is used to compare at constant sampling density the most common of these approximating image tessellations with both a nonregular and a regular hexagonal image tessellation. The test objects were human blood cells, from which features describing cellular geometry were extracted for each image tessellation. Compared to the approximating tessellation, the nonregular tessellation tended to decrease feature means and increase feature variances. In contrast, the regular tessellation tended to increase feature means and decrease feature variances. Consequently, the extracted features showed subtle but consistent differences, with decreasing anisotropic effects and data dispersion for the regular tessellation. In addition, cells contacting others near the 45 degree diagonals were more readily segmented when the image was tessellated on the regular lattice. Expected to be general, these trends recommend use of the regular tessellation, especially when classification accuracy may depend on small differences in several similar geometric features.     

Multi-slice ultrasound image calibration of an intelligent skin-marker for soft tissue artefact compensation

In this paper, a novel multi-slice ultrasound (US) image calibration of an intelligent skin-marker used for soft tissue artefact compensation is proposed to align and orient image slices in an exact H-shaped pattern. Multi-slice calibration is complex, however, in the proposed method, a phantom based visual alignment followed by transform parameters estimation greatly reduces the complexity and provides sufficient accuracy. In this approach, the Hough Transform (HT) is used to further enhance the image features which originate from the image feature enhancing elements integrated into the physical phantom model, thus reducing feature detection uncertainty. In this framework, slice by slice image alignment and calibration are carried out and this provides manual ease and convenience.     

A Novel Method of Automatic Plant Species Identification Using Sparse Representation of Leaf Tooth Features

Automatic species identification has many advantages over traditional species identification. Currently, most plant automatic identification methods focus on the features of leaf shape, venation and texture, which are promising for the identification of some plant species. However, leaf tooth, a feature commonly used in traditional species identification, is ignored. In this paper, a novel automatic species identification method using sparse representation of leaf tooth features is proposed. In this method, image corners are detected first, and the abnormal image corner is removed by the PauTa criteria. Next, the top and bottom leaf tooth edges are discriminated to effectively correspond to the extracted image corners; then, four leaf tooth features (Leaf-num, Leaf-rate, Leaf-sharpness and Leaf-obliqueness) are extracted and concatenated into a feature vector. Finally, a sparse representation-based classifier is used to identify a plant species sample. Tests on a real-world leaf image dataset show that our proposed method is feasible for species identification.     

Noninvasive diagnosis of melanoma with tensor decomposition-based feature extraction from clinical color image

We propose a method for feature extraction from clinical color images, with application in classification of skin lesions. Proposed feature extraction method is based on tensor decomposition of the clinical color image of skin lesion. Since color image is naturally represented as a three-way tensor, it is reasonable to use multi-way techniques to capture the underlying information contained in the image. Extracted features are elements of the core tensor in the corresponding multi-way decomposition, and represent spatial-spectral profile of the lesion. In contrast to common methods that exploit either texture or spectral diversity of the tumor only, the proposed approach simultaneously captures spatial and spectral characteristics. The procedure is tested on a problem of noninvasive diagnosis of melanoma from the clinical color images of skin lesions, with overall sensitivity 82.1% and specificity 86.9%. Our method compares favorably with the state of the art results reported in the literature and provides an interesting alternative to the existing approaches.     

A Novel Vision Localization Method of Automated Micro-Polishing Robot

Based on photogrammetry technology,a novel localization method of micro-polishing robot,which is restricted withincertain working space,is presented in this paper.On the basis of pinhole camera model,a new mathematical model of visionlocalization of automated polishing robot is established.The vision localization is based on the distance-constraints of featurepoints.The method to solve the mathematical model is discussed.According to the characteristics of gray image,an adaptivemethod of automatic threshold selection based on connected components is presented.The center coordinate of the featureimage point is resolved by bilinear interpolation gray square weighted algorithm.Finally,the mathematical model of testingsystem is verified by global localization test.The experimental results show that the vision localization system in working spacehas high precision.     

应用计算机图像技术识别间日疟原虫的初步研究

目的应用数字图像技术识别间日疟原虫。方法基于图像预处理、图像分割、特征提取、判决分类等步骤,分别对间日疟原虫薄血膜的裂殖体图像进行处理,观察其识别效率和准确度。结果边缘检测和图像灰度值检测两种方法均能够识别间日疟原虫裂殖体,而二者的联合方法可以得到较好的识别效果。结论初步探索采用边缘和图像灰度联合检测的方法能够识别间日疟原虫薄血膜的裂殖体。     

Karyotyping of comparative genomic hybridization human metaphases using kernel nearest-neighbor algorithm

BACKGROUND: Comparative genomic hybridization (CGH) is a relatively new molecular cytogenetic method that detects chromosomal imbalances. Automatic karyotyping is an important step in CGH analysis because the precise position of the chromosome abnormality must be located and manual karyotyping is tedious and time-consuming. In the past, computer-aided karyotyping was done by using the 4',6-diamidino-2-phenylindole, dihydrochloride (DAPI)-inverse images, which required complex image enhancement procedures. METHODS: An innovative method, kernel nearest-neighbor (K-NN) algorithm, is proposed to accomplish automatic karyotyping. The algorithm is an application of the "kernel approach," which offers an alternative solution to linear learning machines by mapping data into a high dimensional feature space. By implicitly calculating Euclidean or Mahalanobis distance in a high dimensional image feature space, two kinds of K-NN algorithms are obtained. New feature extraction methods concerning multicolor information in CGH images are used for the first time. RESULTS: Experiment results show that the feature extraction method of using multicolor information in CGH images improves greatly the classification success rate. A high success rate of about 91.5% has been achieved, which shows that the K-NN classifier efficiently accomplishes automatic chromosome classification from relatively few samples. CONCLUSIONS: The feature extraction method proposed here and K-NN classifiers offer a promising computerized intelligent system for automatic karyotyping of CGH human chromosomes.     

Using an Improved SIFT Algorithm and Fuzzy Closed-Loop Control Strategy for Object Recognition in Cluttered Scenes

Partial occlusions, large pose variations, and extreme ambient illumination conditions generally cause the performance degradation of object recognition systems. Therefore, this paper presents a novel approach for fast and robust object recognition in cluttered scenes based on an improved scale invariant feature transform (SIFT) algorithm and a fuzzy closed-loop control method. First, a fast SIFT algorithm is proposed by classifying SIFT features into several clusters based on several attributes computed from the sub-orientation histogram (SOH), in the feature matching phase only features that share nearly the same corresponding attributes are compared. Second, a feature matching step is performed following a prioritized order based on the scale factor, which is calculated between the object image and the target object image, guaranteeing robust feature matching. Finally, a fuzzy closed-loop control strategy is applied to increase the accuracy of the object recognition and is essential for autonomous object manipulation process. Compared to the original SIFT algorithm for object recognition, the result of the proposed method shows that the number of SIFT features extracted from an object has a significant increase, and the computing speed of the object recognition processes increases by more than 40%. The experimental results confirmed that the proposed method performs effectively and accurately in cluttered scenes.